Empirical or interpolation-based approaches treat print devices as a black box between inputs and outputs. Analytical first principle approaches attempt to characterize the device color response using the fewest number of measurements to arrive at analytical functions that physically represent the process. Both approaches are capable of predicting the response of the device for a variety of input images. However, as the number of color separations increases and/or the number of output parameters increase, analytical first principle models require more time and effort to develop. Instead, empirical data-rich methods are utilized. There are advantages in such an approach such as, for example, the ability to better represent the response of a color marking device.
Methods have arisen which attempt to maintain both accuracy and consistency of spot colors while determining optimal color recipes that can provide improved smoothness. All these use either a smoothness model to estimate the smoothness parameters or an insitu full width array (FWA) sensor to measure smoothness so that unwanted image artifacts are minimized. By constraining the color recipe to numerous measurable/predictable image quality parameters, smoothness can be improved in digital color printers. As such, it is useful to introduce additional modeling constraints such that the color recipe becomes unique resulting in the desired output color having improved smoothness.